【摘要】 在从传统力场驱动的蛋白质结构预测到当前数据驱动的AI结构建模的发展历程中，蛋白质结构模板检测是蛋白质结构预测中的关键环节，如何检测高精度蛋白质结构远程模板对提升结构的预测精度具有重要的研究意义。该研究提出了一种基于自适应特征向量提取的远程同源模板检测算法ASEalign。首先，采用多特征信息融合的深度学习技术预测蛋白质接触图；然后，设计了融合接触图、二级结构、序列谱谱比对和溶剂可及性等多维度特征打分函数，并通过自适应地提取接触图矩阵中的特征值和特征向量进行模板比对；最后，将检测出的高质量模板输入AlphaFold2中进行结构建模。在135个蛋白质的测试集上的结果表明，ASEalign相于主流的模板检测算法HHsearch精度提升了11.5%;同时，结构建模的精度优于AlphaFold2。更多还原

【Abstract】 In the development process from traditional force field-driven protein structure prediction to current data-driven AI structure modeling, protein structure template detection is a key module in protein structure prediction, and how to detect high-precision protein structure remote templates is important to improve the prediction accuracy of structures.In this paper, a remote homology template detection algorithm ASEalign based on adaptive eigenvector extraction is proposed.Firstly, a deep learning technique of multi-feature information fusion is used to predict protein contact maps.Then, a multi-dimensional feature scoring function is designed to fuse contact maps, secondary structures, sequence profiles-profiles alignment and solvent accessibility, and the eigenvalue and eigenvector in the contact map matrix extracted by adaptive template alignment is performed.Finally, the detected high-quality templates are input to AlphaFold2 for structural modeling.Results on the test set of 135 proteins indicate that, compared to HHsearch, ASEalign improves the accuracy by 11.5%.Meanwhile, its accuracy of modeled structure is better than that of AlphaFold2.更多还原